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THE CUMULATIVE DISADVANTAGE OF UNEMPLOYMENT Irma Mooi-Reci - - PowerPoint PPT Presentation
THE CUMULATIVE DISADVANTAGE OF UNEMPLOYMENT Irma Mooi-Reci - - PowerPoint PPT Presentation
THE CUMULATIVE DISADVANTAGE OF UNEMPLOYMENT Irma Mooi-Reci University of Melbourne www.melbourneinstitute.com Acknowledgement FUNDING Australian Research Council Discovery Project grant (#DP160101063) COLLABORATORS Anna Manzoni (North
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Motivation: unemployment scarring
§ Research question: how quick do workers’ careers recover from a spell of unemployment? § Common approaches: – Survival models: focus on single transitions (i.e., from unemployment to employment) or competing risk outcomes; – Sequence analytic approaches describe the sequence of post- unemployment transitions;
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Motivation
§ How to quantify recovery in terms of career quality? § Binary sequences:
- Successes (S) vs Failures (F)
Career quality measure: The sum of the position indices of the S-
- bservations quantifies the quality level: the more 𝑇-observations
and/or the more recent these are, the bigger the sum will be.
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Requirements for a new quality measure
a) has a fixed range of [0,1]; b) increases when the number of successes increases; c) decreases when the number of failures increases; d) increases when the number of successes is more recent; e) accounts for the occurrence of successes by means of a weight which captures the fraction of successes relative to the total sequence;
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Implementation: Quality Measure
§ A sequence is successful when a desirable quality/attribute frequently appears towards the end of a sequence
Υ#(𝑦) Υ#(𝑦) 𝑦 𝑥=.5 𝑥=1 𝑥=2 𝑦 𝑥=.5 𝑥=1 𝑥=2 FFFSSS .62 .71 .85 SSSS 1.0 1.0 1.0 FFSFSS .59 .67 .77 SFSS .77 .80 .87 FSFFSS .56 .62 .71 SSFS .72 .70 .70 SFFFSS .53 .57 .68 SSSF .67 .60 .47 SFFSFS .50 .52 .58 FSSF .51 .50 .43 SFSFFS .48 .48 .51 SFSF .44 .40 .33 SSFFFS .45 .43 .45 SSFF .39 .30 .17 SSFFSF .43 .38 .33 FSFF .23 .20 .13 SSFSFF .41 .33 .23 SFFF .16 .10 .03 SSSFFF .38 .29 .15 FFFF .00 .00 .00
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Implementation: Quality Measure
.2 .4 .6 .8 1 Success 10 20 30 40 50 Time Units w=0.5 w=1 w=2
𝒚 = 𝑻𝟓𝑮𝟓𝑻𝟕𝑮𝟑𝑻𝟗𝑮𝟐𝑻𝟑𝟔
The effect of a run of failures depends on its length: the longer the run, the more severe the effect. The bigger the parameter 𝑥, the more severe is the effect of failures, but recovery from the failures due to subsequent successes is also faster for bigger 𝑥
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Data Management
* Make data ready for sequence analysis
bys pid (wave): gen order=_n
* sq-set the data
sqset lfs pid order forvalues x = 1/13 { egen s`x' = sqsuccess(1), w(0.5) subsequence(1,`x’) }
* Create a single success measure
gen s_at_t=. forvalues x=1/13 { replace s_at_t=s`x' if order==`x' }
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Data Management
lfs pid
- rder
N 100001 1 EPT 100001 2 EPT 100001 3 EPT 100001 4 EPT 100002 1 EPT 100002 2 EPT 100002 3 EPT 100002 4 EPT 100003 1 EPT 100003 2 EPT 100003 3 EFT 100003 4 EFT 100003 5 U 100003 6 EFT 100003 7 N 100003 8 EPT 100003 9 EPT 100003 10 N 100003 11 N 100003 12 N 100003 13
Labour Force Status: N = Not in the labour force; EPT = Employed Part-Time; EFT = Employed Full-Time; U = Unemployed;
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Data Management
lfs pid
- rder
success measure N 100001 1 EPT 100001 2 .6666667 EPT 100001 3 .8333333 EPT 100001 4 .9 EPT 100002 1 1 EPT 100002 2 1 EPT 100002 3 1 EPT 100002 4 1 EPT 100003 1 1 EPT 100003 2 1 EPT 100003 3 1 EFT 100003 4 1 EFT 100003 5 1 U 100003 6 .7142857 EFT 100003 7 .7857143 N 100003 8 .6111111 EPT 100003 9 .6888889 EPT 100003 10 .7454545 N 100003 11 .6212121 N 100003 12 .525641 N 100003 13 .4505495
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Application
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Data & Methods
§ Data: German Socio-Economic Panel (GSOEP): 1984-2005 § Sample: Men and women who experienced unemployment sometime
- ver the period 1984 – 2005; (N=152,165 person-year observations;
271 months; 90 trimesters); § Approach: Hybrid Models – Career quality is the DV; – Decomposing time-varying variables into individual-specific means and deviations from those means
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Post unemployment career quality since first unemployment
Coefficient estimates from hybrid models, by Sex.
10 20 30 40 50 60 70 80 90 post unemployment trimester .2 .4 .6 .8 Career Quality Men Women
Career quality over time
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Post unemployment career quality since first unemployment
Coefficient estimates from hybrid models, Men by Age,
10 20 30 40 50 60 70 80 90 post unemployment trimester
- 1
- .5
.5 Career Quality age 18-24 age 25-35 age 36-45 age 46-54 age 55-64
Men by age
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Post unemployment career quality since first unemployment
Coefficient estimates from hybrid models, Women by Age,
10 20 30 40 50 60 70 80 90 post unemployment trimester
- .4
- .2
.2 .4 .6 Career Quality age 18-24 age 25-35 age 36-45 age 46-54 age 55-64
Women by age
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Conclusions and limitations
§ We find a “recovery” trend that applies to both men and women experiencing first unemployment at different ages. § Recovery trends are not monotonic, but instead follow a non-linear trend such that the level of career quality first increases, then slows down and eventually stops. § Women’s recovery in career quality is slower than that of their male counterparts. § Younger men (18-25) and women (before the ages of 35) are more negatively impacted by unemployment compared to those experiencing unemployment at older ages.
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Conclusions and limitations
§ Our measure of career success: – does not account for other employment characteristics – is not suited for career sequences that are more varied and with more than two categories
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APPENDIX
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Behavior of 𝜱𝒙 for different w
𝑦 = 𝑇4𝐺4𝑇6𝐺7𝑇8𝐺9𝑇7:
.2 .4 .6 .8 1 10 20 30 40 50
- rder
w=0.5 w=1 w=2
The effect of a run of failures depends on its length: the longer the run, the more severe the effect. The bigger the parameter 𝑥, the more severe is the effect of failures, but recovery from the failures due to subsequent successes is also faster for bigger 𝑥
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Data Management
lfs pid
- rder
N 100001 1 EPT 100001 2 EPT 100001 3 EPT 100001 4 EPT 100002 1 EPT 100002 2 EPT 100002 3 EPT 100002 4 EPT 100003 1 EPT 100003 2 EPT 100003 3 EFT 100003 4 EFT 100003 5 U 100003 6 EFT 100003 7 N 100003 8 EPT 100003 9 EPT 100003 10 N 100003 11 N 100003 12 N 100003 13
Labour Force Status: N = Not in the labour force; EPT = Employed Part-Time; EFT = Employed Full-Time; U = Unemployed;
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Data Management
lfs pid
- rder
success measure N 100001 1 EPT 100001 2 .6666667 EPT 100001 3 .8333333 EPT 100001 4 .9 EPT 100002 1 1 EPT 100002 2 1 EPT 100002 3 1 EPT 100002 4 1 EPT 100003 1 1 EPT 100003 2 1 EPT 100003 3 1 EFT 100003 4 1 EFT 100003 5 1 U 100003 6 .7142857 EFT 100003 7 .7857143 N 100003 8 .6111111 EPT 100003 9 .6888889 EPT 100003 10 .7454545 N 100003 11 .6212121 N 100003 12 .525641 N 100003 13 .4505495
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